All the approaches, including the deep learning and statistical learning models are firstly evaluated on the Middlebury stereo dataset [1]}, which consists of eight scenes including Art, Books, Bowling, Dolls, Laundry, Moebius, Plastic and Reindeer. We resize the images to \(576 \times 640\)  (for Bowling & Plastic scenes) and \(576 \times 704\)  (for other scenes) pixels resolution. Then, experiments are conducted on real-world data with \(256 \times 256 \times 1536\)  pixels resolution measured by [2]}, which is resized into \(256 \times 256 \times 1024\) . For deep learning models, the evaluations are conducted in a patch-by-patch manner, where each input is cropped into \(128\times 128 \times 1024\)  patches with a stride of 64. After that, the recovered patches are assembled together to produce a full resolution output.
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